The tumor microenvironment (TME) is a highly complex and dynamic ensemble of cells of which a variety of immune cells are a major component. The unparalleled results obtained with immunotherapeutic approaches have underscored the importance of examining the immune landscape of the TME. Recent technological advances have incorporated high-throughput techniques at the single cell level, such as single cell RNA sequencing, mass cytometry, and multi-parametric flow cytometry to the characterization of the TME. Among them, flow cytometry is the most broadly used both in research and clinical settings and multi-color analysis is now routinely performed. The high dimensionality of the data makes the traditional manual gating strategy in 2D scatter plots very difficult. New unbiased visualization techniques provide a solution to this problem. Here we describe the steps to characterize the immune cell compartment in the TME in mouse tumor models by high-parametric flow cytometry, from the experimental setup to the analysis methodology with special emphasis on the use of unsupervised algorithms.
Keywords: Dimensionality reduction algorithm; Flow cytometry; Immune cells; Tumor microenvironment.
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